lesmesrafa / Oil_prediction

In this project different Tme Series techniques are used to predict oil's price besed on historical data

Oil Price Prediction with ARIMA, LSTM, and Prophet

This project uses various time series analysis methods to predict future oil prices based on historical data. The methods used are Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) neural networks, and Facebook's Prophet.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

What you need to install and how to install them:

pandas
numpy
matplotlib
sklearn
tensorflow
keras
statsmodels
fbprophet

You can install the above packages using pip:

pip install pandas numpy matplotlib sklearn tensorflow keras statsmodels fbprophet

Installing

Clone the GitHub repository:

git clone https://github.com/yourusername/oil-price-prediction.git

Navigate to the cloned repository:

cd oil-price-prediction

Results

The ARIMA and LSTM, models provided reasonable predictions for future oil prices. Each model has its strengths and weaknesses, and their performance can vary depending on the specifics of the dataset.

The performance of each model was evaluated using mean squared error (MSE) and visual inspection of the predicted vs. actual price plots.